1. Field of the Invention
The present invention relates generally to an improved method, computer program product, and data processing system. More particularly, the present invention relates to analysis of educational outcomes using cohorts and probabilistic data schemas.
2. Description of the Related Art
Educational methods and outcomes are subject to intensive debate in the United States and other countries. In the United States, a great deal of both Federal and State legislation has been passed in an effort to improve education. However, a great deal of analysis is generated without achieving much learning because educational analysis is not fundamentally data-centric. While statistics and tests may be generated, very little knowledge concerning students, teachers, curricula, and outcomes is generated through extended time periods. For example, factors such as demographics, personality, and learning styles of teachers and students is not taken into account, and educational and life success of individual students is not taken into account. Instead, known solutions regarding educational outcomes tend to be episodic, simple statistical analytics that tend to be used in an outcome-driven manner. In some cases, a criticism of current data gathering techniques in the area of education is that the data gathering techniques and/or the subsequent analysis are designed to support specific outcomes in order to achieve some political objective.
Data processing in many industries, for example, the healthcare industry, may be done at a cohort level. A cohort is a set or group of things or people sharing similar characteristics. See our application Ser. No. 11/404,330, filed Apr. 13, 2006, for a further discussion of the application of cohorts to the healthcare industry.
Use of cohorts can be improved via control cohorts. A control cohort is a group selected from a population that is used as the control group. The control cohort is observed under ordinary conditions while another group is subjected to the hypothetical treatment or other factor being studied. The data from the control group is the baseline against which all other experimental results are measured. For example, a control cohort in a study of medicines for colon cancer may include individuals selected for specified characteristics, such as gender, age, physical condition, or disease state that do not receive the hypothetical treatment. The use and improvement of control cohorts is further described in our application Ser. No. 11/542,397, filed Oct. 3, 2006.
The control cohort is used for statistical and analytical purposes. Particularly, the control cohorts are compared with action or hypothesis cohorts to note differences, developments, reactions, and other specified conditions. Control cohorts are heavily scrutinized by researchers, reviewers, and others that may want to validate or invalidate the viability of a test, hypothetical treatment, or other research. If a control cohort is not selected according to scientifically accepted principles, an entire research project or study may be considered of no validity wasting large amounts of time and money. In the case of education research, selection of a less than optimal control cohort may prevent proving the efficacy of a drug or hypothetical treatment or incorrectly rejecting the efficacy of a drug or hypothetical treatment. In the first case, billions of dollars of potential revenue may be lost. In the second case, a drug or hypothetical treatment may be necessarily withdrawn from marketing when it is discovered that the drug or hypothetical treatment is ineffective or harmful leading to losses in drug development, marketing, and even possible law suits.
As stated above, to date, studies of educational outcomes have been flawed. Additionally, to date, cohorts and control cohorts have not been applied to in-depth, data-centric analysis of educational outcomes.